Yazarlar: İbrahim Ali METİN, Bahadır KARASULU
Anahtar Kelimeler:Human Activities, Deep Learning
Özet: Information can be obtained through classification and recognition systems of human activities at any time. These systems are used in different areas such as disease detection, improvement of physical therapy stages, development of smart home projects, and etc. In this study, data taken from a public data set obtained from accelerometer and gyroscope sensors in smart phones were used. Most of the studies in the literature cannot analyze higher level attributes and their relationships based on time series processing with artificial neural network model. The Long-Short Term Memory (LSTM) model is a very suitable deep learning approach due to its ability to obtain relationships for time series as a recurrent neural network and to be flexible in its layers. The deep learning-based approach that includes this infrastructure has been used in the classification of various human activities in our experiments. In the experiments, different input parameters, layer and network units were given to related network models and classification performance accuracy rate was measured. As a result, a classification performance of approximately 86% to 93% was obtained, showing that six different classes were classified with high accuracy. Discussion and scientific findings are also included in the study.